Existing sensor fault detection processes typically operate on the basis of the hardware redundancy principle and/or the analytical redundancy principle. Hardware redundancy schemes utilize multiple sensors with replicated or correlated readings of signal(s) while analytical redundancy schemes rely on mathematical models of the system being measured to generate an expected value or shape.
In techniques based on hardware redundancy, sensor fault is diagnosed when expected correlation and/or agreement between multiple designated sensors is absent. In particular, measurement models and sensor network models are employed to check whether a sensor within the set of correlated/replicated sensors has failed using techniques such as Principal Component Analysis, Independent Component Analysis, factor analysis or Minimized Mean Squared Error.
In techniques based on analytical redundancy, an expected value of the signal being measured is computed by a computational model of the system. Deviation of the measured signal from this reference, either as an absolute value, or in terms of the shape of the signal, is employed to diagnose sensor fault. The software-based system model needs to produce a reasonably accurate estimate of the variable being measured, and can be developed from physics-based description of the system, from heuristics/rules of thumb represented in artificial intelligence constructs (e.g. fuzzy logic or rule based systems), or deduced from past data of sensor networks known to function perfectly using statistical learning techniques.
Specific examples of the two classes of sensor fault detection in patents and academic publications are set out below.
U.S. Pat. No. 6,016,465 describes a method based on analytical redundancy, where sensor fault is detected with the deviation of measured signal shape from the expected reference instead of the actual value. In the method, outlines of modified measured signal and corresponding modified reference signal over a finite time window are compared by means of a correlation coefficient. The reference signal is generated by a software model of the system under measurement, and modified by a multiplication factor, subtraction of the mean level and any hybrid of values from modified reference signal. Modifications to measured signals are performed in like manner. Both series, i.e. the reference and the measured, are continuously updated with most recent values and their shape compared to within pre specified deviation thresholds.
U.S. Pat. No. 7,286,917 describes a method based on the analytical redundancy principle to detect vehicle speed sensor failure. The system model is provided by a set of heuristics that determine whether the speed measured indicates sensor fault, as described in the flowchart below. When the speed measured is below threshold, fueling rate is greater than threshold, engine speed is greater than minimum threshold, transmission is not in neutral and not in converter mode, at least n number of times then a fault is logged.
U.S. Pat. No. 8,521,354 describes a method to diagnose faults in sensors of airflow mass, O2 intake, fuel injection in an internal combustion engine. The method combines the hardware redundancy principle, i.e. multiple sensors replicate measurement of a key quantity, and analytical redundancy, i.e. an estimation model computes expected values of quantities measured (see FIGS. 3 and 8 of this patent). The EGR values are for example computed with two different methods from different measurements and compared with theoretical values from an EGR map. Fuel injection and airflow mass sensor fault are diagnosed in a simpler comparison with real time calculation of expected values.
U.S. Pat. No. 5,636,137 describes a feedback control which includes a sensor failure detection function especially for but not limited to power assisted steering systems. The method relies on hardware redundancy, with two photo detector units independently measuring LED light indicative of angular displacement or torque applied between input and output. Variation in the sum of the signals generated by the photodetector indicates sensor fault(s).
U.S. Pat. No. 6,598,195 describes a hardware redundancy approach to sensor fault detection. A modeled sensor value of each sensor is given as a function of a set of other sensors values (the sensor consistency model in FIG. 1 of this patent), and any deviation from expected value beyond the threshold indicates a fault is present in the network of sensors. Hypothesis testing and maximum wins strategy isolate the faulty sensor once a fault in the network is diagnosed. Sensor failure accommodation is then performed by substituting the modelled value for the actual measured value.
U.S. Pat. No. 5,554,969 describes an analytical redundancy approach to detect wheel steering angle sensor fault with system model given by rules of thumb. The detection of failure in the rear road wheel steering angle sensor is carried out on the basis of whether a target deviation between a rear road wheel steering angle sensor value and a target rear road wheel steering angle exceeds a predetermined deviation value and whether a generation time duration during which the target deviation exceeds the predetermined deviation value has continued over a predetermined period of time. In addition, the detection of failure in the rear road wheel steering angle sensor is carried out on the basis of whether an accumulated value of a rear road wheel steering angle estimated deviation exceeds a predetermined threshold value a when such conditions as a monotonous variation in a servo current applied to the motor in the four wheel steering system, a monotonous variation in a rear road wheel steering angle estimated deviation, and same directional monotonous variations in the servo current and in the rear road wheel steering angle estimated deviation are satisfied
Silva, J C et al. “A knowledge based system approach for sensor fault modelling, detection and mitigation.” Expert Systems with Applications, 2012, pp 10977-10989 describes a combination of semantic network, object oriented models and rules to detect common sensor faults (bias, drift, scaling), derived from an Artificial Neural Network (ANN) for fault detection and disambiguation. The ANN is a machine learning technique that learns to accurately detect and disambiguate fault from data. Sensor fault detection is performed with analytical redundancy and fault mitigation is performed by utilizing partial sensor redundancy and sensor correction.
Xu, X. et al. “Online sensor calibration monitoring and fault detection for chemical processes.” Maintenance and Reliability Conference, 1998 describes an artificial neural network for instrument surveillance and calibration verification system, given in the following figure. The ANN essentially models the system and produces an estimate to be compared with (corrected) measured signal. The difference between the two is fed into a statistical decision module that diagnoses a fault or otherwise based on the profile of the variance and mean of the residual.
Rajagopal, R. et al. “Distributed online simultaneous fault detection for multiple sensors.” Information Processing in Sensor Networks, 2008 presents a distributed, online, sequential algorithm for detecting multiple faults in a sensor network for a time varying noisy environment. The algorithm works by detecting change points in the correlation statistics of neighbouring sensors, requiring only neighbours to exchange information. The algorithm provides guarantees on detection delay and false alarm probability. The method utilizes hardware redundancy and requires that functioning neighboring sensors to measure correlated variables, that the measurements of a faulty sensor and a neighboring working sensor are not correlated, and that the average time between successive faults to be longer than the time between significant changes in the environment.
Kulla, J. “Detection, identification and quantification of sensor fault.” Mechanical Systems and Signal Processing, October 2013, vol. 40(1), pp. 208-221 describes a hardware redundancy approach for detecting bias, gain, drift, precision degradation, noise and complete failure by modeling each sensor as a conditional probability density function given the other sensors in the network. Each sensor in the network is modeled using the minimum mean squared error estimation and sensor fault identified using multiple hypothesis test with generalized likelihood ratio.
Hardware redundancy techniques generally require replication and/or sensor models that remain valid in practice. If the sensor models are not satisfied then the method will fail. Hardware redundancy is also expensive (normally due to the replication).
Analytical redundancy techniques rely on the estimated value/shape of the measured variable and therefore normally require reasonably accurate system models. Developing such models for very complex systems from the basic physics can involve high computational cost and may not be possible at all for some complex systems that operate in a partially observable, time-varying environment like a marine vessel. Heuristics or rules of thumb of overall system behaviour can be developed into reasonably robust models of how the system should behave but are normally very application specific and require a lengthy period of observation. Also, observation noise and a time varying environment can easily lead to false positives or true negatives in analytical redundancy approaches. Learning the model from data is an attractive option but again requires a large amount of data and will not perform well when the situation at hand deviates significantly from the training data distribution.
Typically, the prior art requires a good system model and/or sensor model and/or robust rules of thumb and/or extensive high quality operational data to work.